similar to: recommended computing server for R (March 2009)?

Hi there, I am running Monte Carlo Simulations in R using ordinary "while (condition)" loops. Since the number of iterations is something like 100.000 and within each iteration a given subsample is extended sequentially it takes hours to run the simulation. Does anyone know if there is either a way to avoid using loops in Monte Carlo Simulations or how to include possible faster

Dear list users, I need to learn about MCMC methods, and since there are several packages in R that deal with this subject, I want to use them. I want to buy a book (or more than one, if necessary) that satisfies the following requirements: - it teaches well MCMC methods; - it is easy to implement numerically the ideas of the book, and notation and concepts are similar to the corresponding R

The release of a new code generator in Mono 2.2 prompted me to benchmark the performance of various VMs using the SciMark2 benchmark on an 8x 2.1GHz 64-bit Opteron and I have published the results here: http://flyingfrogblog.blogspot.com/2009/01/mono-22.html The LLVM results were generated using llvm-gcc 4.2.1 on the C version of SciMark2 with the following command-line options: llvm-gcc

On Wed, 19 Sep 2018 at 13:43, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > > I think the analyses are correct, but I doubt if a change to the default > is likely to be accepted as it would make it more difficult to reproduce > older results. I'm a bit alarmed by the logic here. Unbiased sampling seems basic for a statistical language. As a consumer of R I'd

Dear R helpers Suppose I have a portfolio of securities with exposure to Equity, Bonds and Forex (say $ 1000000 each). Is there any fucntion in R that will help me calculate Value at Risk (VaR) using Monte carlo Simulation , Historic simulation and Variance - Covariance Simulation. With regards Maithili

I normally use the following code to create a figure displaying the mark correlation function for the point pattern process "A": M<-markcorr(A) plot(M) I have now started to use the following code to perform 1000 Monte Carlo simulations of Complete Spatial Randomness (CSR). It is a Monte Carlo test based on envelopes of the Mark correlation function obtained from simulated point

Hi, all, I'm running a monte carlo simulation with missing data. The data are arranged such that there are k columns and n rows over a set number of simulations (set to 10 right now so it runs fast while I set everything up). The data are integers, numbers 1-7 only (normal distribution). The simulations are set up and run without a hitch, including imposing NA missing values at a specified

Hello, currently I'm working on a model based on Monte-Carlo-Simulations. I observed that a generated normal distributed times series using rnorm(100,mean=0,sd=1) is far away from being not autocorrelated. Is there any other gerenator implemented in R, which might solve my problem? -- View this message in context:

Dear R users, I'm working with Windows 2000 and R -- note that I could maybe work with Linux too... I would like to know if it is possible to build a stand-alone statistical package which: -- could be programmed in R language, -- could have a nice graphical interface with buttons and menus (this package will be distributed to non-statistician people), -- could realize statistical operations,

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Hi all, I'm trying to estimate a nested model (purchase decision, cloglog formula, & quantity bought given a purchase, truncated Poisson). Some of the parameters are mixed (6) and 4 are fixed for all the respondent. The simulated ML (500 simulations) method forwards highly correlated estimates. After some research, Hybrid Monte Carlo seems to be a good alternative to estimate the model. I

Hi, Can anyone help me with the following. I have been using R for Monte Carlo simulations and got some results I couldn't explain. Therefor I performed following short test: -------------- mean.sds <- NULL sample.sizes <- 3:30 for(N in sample.sizes){ dum <- NULL for(I in 1:5000){ x <- rnorm(N,0,1) dum <- c(dum,sd(x)) } mean.sds<- c(mean.sds,mean(dum)) }

Dear R Experts--- Sorry for all the questions yesterday and today. I am trying to use Yves Croissant's pgmm function in the plm package with Blundell-Bond moments. I have read the Blundell-Bond paper, and want to run the simplest model first, d[i,t] = a*d[i,t-1] + fixed[i] + u[i,t] . no third conditioning variables yet. the full set of moment conditions recommended for system-GMM,

List, I have looked through several R books and searched the web to find answers to my questions with no results. I have a ensembles of time series data (essentially from Monte Carlo simulations) which I would like to summarize as a time series of boxplots. I don't know how to do this and I am not sure how I should structure the data to get what I want. Another related question: while

Full_Name: Reginaldo Constantino Version: 2.8.0 OS: Ubuntu Hardy (32 bit, kernel 2.6.24) Submission from: (NULL) (189.61.88.2) For many tables, chisq.test with simulate.p.value=TRUE gives a p value that is obviously incorrect and inversely proportional to the number of replicates: > data(HairEyeColor) > x <- margin.table(HairEyeColor, c(1, 2)) >

Hello! I am running a very simple mini Monte-Carlo below using the function tstatistic (right below this sentence): tstatistic = function(x,y){ m=length(x) n=length(y) sp=sqrt( ((m-1)*sd(x)^2 + (n-1)*sd(y)^2)/(m+n-2) ) t=(mean(x)-mean(y))/(sp*sqrt(1/m+1/n)) return(t) } alpha=.1; m=10; n=10 # sets alpha, m, n - for run 1 N=10000 # sets the number of simulations n.reject=0 # counter of num.

Hi, We have recently published a paper on floating point accuracy analysis through Monte Carlo Arithmetic. We also released the open-source tool Verificarlo (https://github.com/verificarlo/verificarlo) that relies on LLVM for instrumenting floating point operations. Could you please add our paper to http://llvm.org/pubs/ ? Verificarlo: checking floating point accuracy through Monte Carlo

Dear all, I have a .dta database which is about 400 MB. I cannot open it though I have no problem to import smaller ones (80 MB or even 174 MB). I tried to modify some options with --max-mem-size=2047M --max-vsize=2047M. But it does not seem to be enough. I do not know the exact meaning of these options : vsize seems to be made for vectors. I have got Monte Carlo simulations running in another

Full_Name: Alessandra Iacobucci Version: 2.5.1 OS: Mac OS X 10.4.11 Submission from: (NULL) (193.48.71.92) Hi, I am making some intensive simulations for the testing of a Population Monte Carlo algorithm. This involves also a study of the CPU times in two different case. What I am trying to measure is the "real" CPU time, the one which is independent on the %CPU. I'm using the

Hello, I am doing simulations, and I generate a list at each iteration (with three component matrices in the example below), saving the results in a list. For example, after two iterations, I have something like > str(sim.theta) List of 2 $ :List of 3 ..$ : num [1:6, 1:4] -3.67 -1.07 -2.99 -18.38 -3.26 ... ..$ : num [1:6, 1:6] -7.56 -3.14 -4.99 1.03 2.79 ... ..$ : num [1:6, 1:4]